Data warehousing and online analytical processing (OLAP) are widespread technologies employed to support business decisions and data analysis. A data warehouse is a nonvolatile repository for an enormous volume of organizational or enterprise information (e.g., 100 MB-TB). These data warehouses are populated at regular intervals with data from one or more heterogeneous data sources, for example from multiple transactional systems. This aggregation of data provides a consolidated view of an organization from which valuable information can be derived. Though the sheer volume can be overwhelming, the organization of data can help ensure timely retrieval of useful information.
Data warehouse data is often stored in accordance with a multidimensional database model or cube. A cube has two major modeling components, namely measure groups or measures and dimensions. Measures represent real values or factual data that can be analyzed. For instance, measures can correspond to sales or the number of units sold. Dimensions represent qualitative information about data, such as time or product. Stated differently, a dimension describes an entity by which a user wants to analyze their data. A dimension is a collection of attributes that source the dimension's hierarchies and member properties. For example, a time dimension can include distinct attributes including a set of members such as year, month and day, which can be employed to define a dimension hierarchy. Accordingly, data can be viewed or navigated at different levels of detail.
There are at least two primary reasons for the existence of multidimensional databases. First, the multidimensional model is optimized to deal with large amounts of data. In other words, it has a fast query response over large data volumes. Furthermore, the multidimensional model is business user friendly. This enables users execute complex queries on a data cube and retrieve valuable information. Today, OLAP is almost synonymous with multidimensional databases.
OLAP is a key element in a data warehouse system. OLAP describes a category of technologies or tools utilized to retrieve data from a data warehouse. These tools can extract and present multidimensional data from different points of view to assist and support managers and other individuals examining and analyzing data. The multidimensional data model is advantageous with respect to OLAP as it allows users to easily formulate complex queries, and filter or slice data into meaningful subsets, among other things. There are two basic types of OLAP architectures MOLAP and ROLAP. MOLAP (Multidimensional OLAP) utilizes a true multidimensional database to store data. ROLAP (Relational OLAP) utilizes a relational database to store data but is mapped so that an OLAP tool sees the data as multidimensional. HOLAP (Hybrid OLAP) is an amalgam of both MOLAP and ROLAP.